Medical Image Segmentation Based on Mutual Information Maximization

  • Jaume Rigau
  • Miquel Feixas
  • Mateu Sbert
  • Anton Bardera
  • Imma Boada
Conference paper

DOI: 10.1007/978-3-540-30135-6_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3216)
Cite this paper as:
Rigau J., Feixas M., Sbert M., Bardera A., Boada I. (2004) Medical Image Segmentation Based on Mutual Information Maximization. In: Barillot C., Haynor D.R., Hellier P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg

Abstract

In this paper we propose a two-step mutual information-based algorithm for medical image segmentation. In the first step, the image is structured into homogeneous regions, by maximizing the mutual information gain of the channel going from the histogram bins to the regions of the partitioned image. In the second step, the intensity bins of the histogram are clustered by minimizing the mutual information loss of the reversed channel. Thus, the compression of the channel variables is guided by the preservation of the information on the other. An important application of this algorithm is to preprocess the images for multimodal image registration. In particular, for a low number of histogram bins, an outstanding robustness in the registration process is obtained by using as input the previously segmented images.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jaume Rigau
    • 1
  • Miquel Feixas
    • 1
  • Mateu Sbert
    • 1
  • Anton Bardera
    • 1
  • Imma Boada
    • 1
  1. 1.Institut d’Informatica i AplicacionsUniversitat de GironaSpain

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